This project focuses on developing a machine learning model to detect heart disease based on various medical attributes. It aims to provide an efficient and accurate tool for early diagnosis, potentially improving patient outcomes through timely intervention. Note: the project is till in it's initial stage and does not represent the final project.
- Data preprocessing and analysis
- Machine learning model training and evaluation
- User-friendly interface for inputting patient data (to be added)
- Real-time prediction of heart disease risk (to be implemented)
- Python 3.x
- Pandas for data manipulation
- Scikit-learn for machine learning algorithms
- NumPy for numerical operations
- Matplotlib and Seaborn for data visualization
- Flask for web application backend (optional)
- HTML/CSS/JavaScript for frontend (optional)
- Clone the repository:
git clone https://github.com/yourusername/heart-disease-detection.git - Install required packages:
pip install -r requirements.txt
- Run the main script:
python main.py - Follow the prompts to input patient data or use the web interface if implemented.
Our current model achieves an accuracy of X% on the test set. We continuously work on improving its performance through feature engineering and algorithm optimization.
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE.md file for details.
- Dataset provided by https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset
- Inspiration from various youtube videos and machine learning articles